IITR-CIOL@NLU of Devanagari Script Languages 2025: Multilingual Hate Speech Detection and Target Identification in Devanagari-Scripted Languages

Siddhant Gupta, Siddh Singhal, Azmine Toushik Wasi
12/23/2024
cs.CL

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Abstract

This work focuses on two subtasks related to hate speech detection and target identification in Devanagari-scripted languages, specifically Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit. Subtask B involves detecting hate speech in online text, while Subtask C requires identifying the specific targets of hate speech, such as individuals, organizations, or communities. We propose the MultilingualRobertaClass model, a deep neural network built on the pretrained multilingual transformer model ia-multilingual-transliterated-roberta, optimized for classification tasks in multilingual and transliterated contexts. The model leverages contextualized embeddings to handle linguistic diversity, with a classifier head for binary classification. We received 88.40% accuracy in Subtask B and 66.11% accuracy in Subtask C, in the test set.

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Cite this paper

@article{gupta2024iitrciolnlu,
  title  = {IITR-CIOL@NLU of Devanagari Script Languages 2025: Multilingual Hate Speech Detection and Target Identification in Devanagari-Scripted Languages},
  author = {Siddhant Gupta and Siddh Singhal and Azmine Toushik Wasi},
  year   = {2024},
  eprint = {2412.17947},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.17947v2}
}

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